import argparse
import os
import ruamel_yaml as yaml
import numpy as np
import random
import time
import datetime
import json
from pathlib import Path
from models.tokenization_bert import BertTokenizer

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torch.distributed as dist
from torch.utils.data import DataLoader

from models.model_retrieval_mplug import MPLUG
from models.vit import interpolate_pos_embed, resize_pos_embed
import utils
from dataset.video_dataset import VideoDataset


@torch.no_grad()
def evaluation(model, data_loader, tokenizer, device, config):
    # test
    model.eval()

    metric_logger = utils.MetricLogger(delimiter="  ")
    header = 'Evaluation:'

    print('Computing features for evaluation...')
    start_time = time.time()

    texts = data_loader.dataset.text
    num_text = len(texts)
    text_bs = 256
    text_feats = []
    text_embeds = []
    text_atts = []
    for i in range(0, num_text, text_bs):
        text = texts[i: min(num_text, i + text_bs)]
        text_input = tokenizer(text, padding='max_length', truncation=True, max_length=35, return_tensors="pt").to(
            device)
        text_output = model.text_encoder(text_input.input_ids, attention_mask=text_input.attention_mask)
        text_feat = text_output.last_hidden_state
        text_embed = F.normalize(model.text_proj(text_output.last_hidden_state[:, 0, :]))
        text_embeds.append(text_embed)
        text_feats.append(text_feat)
        text_atts.append(text_input.attention_mask)

    text_embeds = torch.cat(text_embeds, dim=0)
    text_feats = torch.cat(text_feats, dim=0)
    text_atts = torch.cat(text_atts, dim=0)

    video_feats = []
    video_embeds = []
    for video, video_id in data_loader:
        B, N, C, W, H = video.size()
        video = video.view(-1, C, W, H)
        video = video.to(device, non_blocking=True)
        video_feat = model.visual_encoder.visual(video, skip_last_layer=True)
        video_feat = model.visn_layer_norm(model.visn_fc(video_feat))
        video_embed = model.vision_proj(video_feat[:, 0, :])
        video_embed = video_embed.view(B, N, -1).mean(dim=1)
        video_embed = F.normalize(video_embed, dim=-1)

        video_feat = video_feat.view(B, -1, video_feat.shape[-1])
        video_feats.append(video_feat.cpu())
        video_embeds.append(video_embed)

    video_feats = torch.cat(video_feats, dim=0)
    video_embeds = torch.cat(video_embeds, dim=0)

    sims_matrix = video_embeds @ text_embeds.t()
    score_matrix_v2t = torch.full((len(texts), len(texts)), -100.0).to(device)

    num_tasks = utils.get_world_size()
    rank = utils.get_rank()
    step = sims_matrix.size(0) // num_tasks + 1
    start = rank * step
    end = min(sims_matrix.size(0), start + step)

    for i, sims in enumerate(metric_logger.log_every(sims_matrix[start:end], 50, header)):
        topk_sim, topk_idx = sims.topk(k=config['k_test'], dim=0)

        encoder_output = video_feats[start + i].repeat(config['k_test'], 1, 1).to(device, non_blocking=True)
        encoder_att = torch.ones(encoder_output.size()[:-1], dtype=torch.long).to(device, non_blocking=True)
        _, output = model.fusion_encoder(encoder_embeds=text_feats[topk_idx],
                                         attention_mask=text_atts[topk_idx],
                                         encoder_hidden_states=encoder_output,
                                         encoder_attention_mask=encoder_att,
                                         return_dict=False,
                                        )
        score = model.itm_head(output[:, 0, :])[:, 1]
        score_matrix_v2t[start + i, topk_idx] = score + topk_sim

    sims_matrix = sims_matrix.t()
    score_matrix_t2v = torch.full((len(texts), len(texts)), -100.0).to(device)

    step = sims_matrix.size(0) // num_tasks + 1
    start = rank * step
    end = min(sims_matrix.size(0), start + step)

    for i, sims in enumerate(metric_logger.log_every(sims_matrix[start:end], 50, header)):
        topk_sim, topk_idx = sims.topk(k=config['k_test'], dim=0)
        encoder_output = video_feats[topk_idx].to(device, non_blocking=True)
        encoder_att = torch.ones(encoder_output.size()[:-1], dtype=torch.long).to(device, non_blocking=True)
        _, output = model.fusion_encoder(encoder_embeds=text_feats[start + i].repeat(config['k_test'], 1, 1),
                                      attention_mask=text_atts[start + i].repeat(config['k_test'], 1),
                                      encoder_hidden_states=encoder_output,
                                      encoder_attention_mask=encoder_att,
                                      return_dict=False,
                                     )
        score = model.itm_head(output[:, 0, :])[:, 1]
        score_matrix_t2v[start + i, topk_idx] = score + topk_sim

    if args.distributed:
        dist.barrier()
        torch.distributed.all_reduce(score_matrix_v2t, op=torch.distributed.ReduceOp.SUM)
        torch.distributed.all_reduce(score_matrix_t2v, op=torch.distributed.ReduceOp.SUM)

    total_time = time.time() - start_time
    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
    print('Evaluation time {}'.format(total_time_str))

    return score_matrix_v2t.cpu().numpy(), score_matrix_t2v.cpu().numpy()


@torch.no_grad()
def itm_eval(scores_v2t, scores_t2v, txt2vmg, vid2txt):
    # Video->Text
    ranks = np.zeros(scores_v2t.shape[0])
    for index, score in enumerate(scores_v2t):
        inds = np.argsort(score)[::-1]
        ranks[index] = np.where(inds == vid2txt[index])[0][0]

    # Compute metrics
    tr1 = 100.0 * len(np.where(ranks < 1)[0]) / len(ranks)
    tr5 = 100.0 * len(np.where(ranks < 5)[0]) / len(ranks)
    tr10 = 100.0 * len(np.where(ranks < 10)[0]) / len(ranks)

    # Text->Video
    ranks = np.zeros(scores_t2v.shape[0])

    for index, score in enumerate(scores_t2v):
        inds = np.argsort(score)[::-1]
        ranks[index] = np.where(inds == txt2vmg[index])[0][0]

    mdR = np.median(ranks + 1)

    # Compute metrics
    vr1 = 100.0 * len(np.where(ranks < 1)[0]) / len(ranks)
    vr5 = 100.0 * len(np.where(ranks < 5)[0]) / len(ranks)
    vr10 = 100.0 * len(np.where(ranks < 10)[0]) / len(ranks)

    tr_mean = (tr1 + tr5 + tr10) / 3
    vr_mean = (vr1 + vr5 + vr10) / 3
    r_mean = (tr_mean + vr_mean) / 2

    eval_result = {'txt_r1': tr1,
                   'txt_r5': tr5,
                   'txt_r10': tr10,
                   'txt_r_mean': tr_mean,
                   'vid_r1': vr1,
                   'vid_r5': vr5,
                   'vid_r10': vr10,
                   'vid_r_mean': vr_mean,
                   'vid_mdR': mdR,
                   'r_mean': r_mean}
    return eval_result


def main(args, config):
    utils.init_distributed_mode(args)

    device = torch.device(args.device)

    # fix the seed for reproducibility
    seed = args.seed + utils.get_rank()
    torch.manual_seed(seed)
    np.random.seed(seed)
    random.seed(seed)
    cudnn.benchmark = True

    #### Dataset ####
    print("Creating retrieval dataset")
    test_dataset = VideoDataset(config['video_root'], config['ann_root'], num_frm=config['num_frm_test'],
                                max_img_size=config['image_size'], frm_sampling_strategy='uniform')

    test_loader = DataLoader(
        test_dataset,
        batch_size=config['batch_size'],
        num_workers=4,
        pin_memory=True,
        drop_last=False,
        shuffle=False,
    )

    #### Model ####
    print("Creating model")
    tokenizer = BertTokenizer.from_pretrained(args.text_encoder)
    model = MPLUG(config=config, tokenizer=tokenizer)

    if args.checkpoint:
        checkpoint = torch.load(args.checkpoint, map_location='cpu')
        try:
            state_dict = checkpoint['model']
        except:
            state_dict = checkpoint['module']

        # reshape positional embedding to accomodate for image resolution change

        if config["clip_name"] == "ViT-B-16":
            num_patches = int(config["image_res"] * config["image_res"] / (16 * 16))
        elif config["clip_name"] == "ViT-L-14":
            num_patches = int(config["image_res"] * config["image_res"] / (14 * 14))
        pos_embed = nn.Parameter(torch.zeros(num_patches + 1, 768).float())

        pos_embed = resize_pos_embed(state_dict['visual_encoder.visual.positional_embedding'].unsqueeze(0),
                                               pos_embed.unsqueeze(0))
        state_dict['visual_encoder.visual.positional_embedding'] = pos_embed

        for key in list(state_dict.keys()):
            if ('fusion' in key or 'bert' in key) and 'decode' not in key:
                encoder_key = key.replace('fusion.', '').replace('bert.', '')
                state_dict[encoder_key] = state_dict[key]
                del state_dict[key]

        msg = model.load_state_dict(state_dict, strict=False)
        print('load checkpoint from %s' % args.checkpoint)
        print(msg)

    model = model.to(device)

    model_without_ddp = model
    if args.distributed:
        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
        model_without_ddp = model.module

    score_v2t, score_t2v, = evaluation(model_without_ddp, test_loader, tokenizer, device, config)

    if utils.is_main_process():
        test_result = itm_eval(score_v2t, score_t2v, test_loader.dataset.txt2video, test_loader.dataset.video2txt)
        print(test_result)

        log_stats = {**{f'{k}': v for k, v in test_result.items()}, }
        with open(os.path.join(args.output_dir, "test_result.txt"), "a") as f:
            f.write(json.dumps(log_stats) + "\n")


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--config', default='./configs/retrieval_msrvtt.yaml')
    parser.add_argument('--output_dir', default='output/Retrieval_msrvtt')
    parser.add_argument('--device', default='cuda')
    parser.add_argument('--text_encoder', default='bert-base-uncased')
    parser.add_argument('--checkpoint', default='')
    parser.add_argument('--seed', default=42, type=int)
    parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes')
    parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
    parser.add_argument('--distributed', default=True, type=bool)
    args = parser.parse_args()

    config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader)
    config['text_encoder'] = args.text_encoder

    Path(args.output_dir).mkdir(parents=True, exist_ok=True)

    yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w'))

    main(args, config)
